Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add datarobot-oss/datarobot-agent-skills --skill datarobot-model-monitoringgit clone --depth 1 https://github.com/datarobot-oss/datarobot-agent-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/datarobot-oss/datarobot-agent-skills/datarobot-model-monitoring)<a href="https://agentmods.dev/skills/datarobot-oss/datarobot-agent-skills/datarobot-model-monitoring"><img src="https://agentmods.dev/badge/skills/datarobot-oss/datarobot-agent-skills/datarobot-model-monitoring/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/datarobot-oss/datarobot-agent-skills/datarobot-model-monitoring"><img src="https://agentmods.dev/badge/skills/datarobot-oss/datarobot-agent-skills/datarobot-model-monitoring.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00042 | $0.01603 |
| Opus 5 | $0.00021 | $0.00801 |
| Sonnet 5 | $0.00008 | $0.00321 |
| Haiku 4.5 | $0.00004 | $0.00160 |
Grade A, and why
datarobot-model-monitoring scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 226 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DataRobot Model Monitoring Skill
This skill provides comprehensive guidance for monitoring deployed models, tracking performance metrics, detecting data drift, and managing model health.
Quick Start
Most common use case: Check deployment health and data drift
- Check service stats:
deployment.get_service_stats(...)to review prediction volume/latency - Check drift:
deployment.get_feature_drift(...)/deployment.get_target_drift(...) - Compare over time: Use
get_service_stats_over_time(...)and drift periods to assess trends
Example: "Check the health of deployment abc123 and report any data drift issues"
When to use this skill
Use this skill when you need to:
- Monitor model performance in production
- Track data drift and feature drift
- Detect prediction anomalies
- Monitor prediction accuracy over time
- Set up alerts for model degradation
- Analyze model health metrics
- Compare production performance to training performance
Key capabilities
1. Performance Monitoring
- Track prediction accuracy and metrics over time
- Compare production metrics to training metrics
- Monitor prediction volume and latency
- Identify performance degradation trends
2. Data Drift Detection
- Detect changes in feature distributions
- Identify feature drift (statistical changes)
- Monitor target drift (if actuals available)
- Alert on significant drift events
3. Prediction Monitoring
- Monitor prediction distributions
- Detect prediction anomalies
- Track prediction confidence scores
- Identify unusual prediction patterns
4. Health Management
- Assess overall model health
- Generate monitoring reports
- Set up automated alerts
- Manage model retraining triggers
Workflow examples
Example 1: Check model health and drift
User request: "Check the health of deployment abc123 and report any data drift issues."
Agent workflow:
- Get deployment monitoring status
- Retrieve recent performance metrics
- Check for data drift in key features
- Compare current metrics to baseline (training)
- Identify any significant drift or degradation
- Report findings with recommendations
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 226 lines · 42 tokens per session scan A 9e36e406af0a
datarobot-model-monitoring is a skill published in the GitHub repository datarobot-oss/datarobot-agent-skills (25 stars, last pushed yesterday), licensed Apache-2.0. It adds 42 tokens to every session and 1,603 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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